Gabriele Baris
Papers
4
Total Citations
37
H-Index
3
About
Gabriele Baris is a robotics researcher advancing autonomous aerial systems, with a focus on exploration, perception, and mission planning for unmanned aerial vehicles (UAVs). His work addresses critical challenges in enabling UAVs to operate intelligently in unknown and GPS-denied environments. Baris’s most influential paper, “An Efficient Object-Oriented Exploration Algorithm for Unmanned Aerial Vehicles” (14 citations), introduces a novel paradigm that shifts from maximizing volumetric coverage to minimizing the time needed to locate specific objects of interest—a key distinction for search-and-rescue and inspection tasks. He further developed a comprehensive framework for autonomous UAV missions in partially unknown, GNSS-denied settings (12 citations), demonstrating how low-cost drones can execute complex missions without human intervention. Baris also contributed to multi-camera extrinsic calibration for real-time tracking in large outdoor environments (8 citations), enabling robust perception across distributed camera networks. His recent work on learning heuristics for environment exploration using graph neural networks (3 citations) points toward data-driven approaches to improve planning efficiency. Through these contributions, Baris is helping to make autonomous UAVs more capable, reliable, and practical for real-world deployment.
Research Focus
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Top Papers
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